Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.

With increasing technological demand for portable electronic and photovoltaic devices, it has become critical to ensure the electrical and mechano-electric reliability of electrodes in such devices. However, the limited flexibility and high processing costs of traditional electrodes based on indium tin oxide undermine their application in flexible devices. Among various alternative materials for flexible electrodes, such as metallic/carbon nanowires or meshes, silver nanowire (Ag NW) networks are regarded as promising candidates owing to their excellent electrical, optical, and mechano-electric properties. In this context, there have been tremendous studies on the physico-chemical and mechano-electric properties of Ag NW networks. At the same time, it has been a crucial job to maximize the device performance (or their mechano-electric performance) by reconciliation of various properties. This review discusses the properties and device applications of Ag NW networks under dynamic motion by focusing on notable findings and cases in the recent literature. Initially, we introduce the fabrication (deposition process) of Ag NW network-based electrodes from solution-based coating processes (drop casting, spray coating, spin coating, etc.) to commercial processes (slot-die and roll-to-roll coating). We also discuss the electrical/optical properties of Ag NW networks, which are governed by percolation, and their electrical contacts. Second, the mechano-electric properties of Ag NW networks are reviewed by describing individual and combined properties of NW networks with dynamic motion under cyclic loading. The improved mechano-electric properties of Ag NW network-based flexible electrodes are also discussed by presenting various approaches, including post-treatment and hybridization. Third, various Ag NW-based flexible devices (electronic and optoelectronic devices) are introduced by discussing their operation principles, performance, and challenges. Finally, we offer remarks on the challenges facing the current studies and discuss the direction of research in this field, as well as forthcoming issues to be overcome to achieve integration into commercial devices.

We demonstrate a method for controlling whether the block copolymer (BCP) cylinders comprising a thin film self-assembled on a Si substrate are oriented parallel or vertical to the substrate. This was accomplished by fabricating topographic patterns having different trench/mesa ratios on the Si substrate and resulting in the dewetting behavior of the BCP film on the mesa regions of the substrate, which led to the migration of the BCP mass from the mesas to the trenches owing to capillary forces during thermal annealing. By increasing the width of the mesas, the thickness of the BCP films formed in the trenches could be increased. Thus, by increasing the trench/mesa ratio from 1 : 1 to 1 : 3, the thickness of the BCP film in the trenches could be increased with precision from 18 to 26 nm. Films comprising arrays of parallelly oriented cylinders were preferentially formed with thicknesses of less than 21.5 nm. For films greater than 22.5 nm in thickness, the orientation of the cylinders switched and the cylinders became vertically oriented in the absence of interfacial sublayers between the BCP film and the topographic guide patterns on the Si substrate.

A photo-crosslinked polystyrene (PS) thin film is investigated as a potential guiding sub-layer for polystyrene-block-poly (methyl methacrylate) block copolymer (BCP) cylindrical nanopattern formation via topographic directed self-assembly (DSA). When compared to a non-crosslinked PS brush sub-layer, the photo-crosslinked PS sub-layer provided longer correlation lengths of the BCP nanostructure, resulting in a highly uniform DSA nanopattern with a low number of BCP dislocation defects. Depending on the thickness of the sub-layer used, parallel or orthogonal orientations of DSA nanopattern arrays were obtained that covered the entire surface of patterned Si substrates, including both trench and mesa regions. The design of DSA sub-layers and guide patterns, such as hardening the sub-layer by photo-crosslinking, nano-structuring on mesas, the relation between trench/mesa width, and BCP equilibrium period, were explored with a view to developing defect-reduced DSA lithography technology.